Model-based pose estimation from a monocular camera consists of estimating the position and orientation of a calibrated camera with respect to a known model, under the assumption that a set of features (such as 3D model points, lines, or conics) and their images points are given. Pose estimation is a fundamental problem in computer vision and can find its applications in visual servoing, pattern recognition, camera calibration, and etc.
The literature on pose estimation is extensive and falls into two categories: iterative algorithms and noniterative algorithms. The noniterative algorithms such as the algorithm of Gerald Schweighofer and Axel Pinz, the iterative algorithms such as the method of Oberkampf et al and the method of L. S. Davis et al., all these algorithms require known correspondences, i.e., the known points or lines correspondences. In addition, there are some algorithms which attempt to solve the pose and correspondences simultaneously, the typical example is the SoftPosit algorithm.
The existing methods can handle the simultaneous pose and correspondences determination given a 3D model, such as the SoftPosit algorithm. However, it fails in the presence of a planar model because it cannot cope with the pose redundancy, which states that the estimated poses increase exponentially as iterations continue.
As stated above, the applications of the existing pose estimation algorithms are limited by the requirements of known points-correspondences or a 3D model. In view of the above-mentioned problems, particularly the pose redundancy problem that the number of the estimated poses increase exponentially as iterations go for a planar model, we disclose in the invention an effective method and a software to solve the simultaneous pose and points-correspondences determination for a planar model.